We choose GCN [1]} as the base GNN within our architecture. Nonetheless, we consider few simple modifications to explore sufficiently complex model configurations. The GCN layer is mainly described via two distinct operations: (i) aggregation via message passing and (ii) node encoding update. Our implementation follows a variant of Eq. REF :
\(H^{t+1} = RNN_{cell}(MLP(A, H^t; \theta ^{t+1}), H^t)\) 
